A Noise-Robust Method with Smoothedℓ1/ℓ2Regularization for Sparse Moving-Source Mapping

Autor: Jrme I. Mars, Mai Quyen Pham, Benoit Oudompheng, Barbara Nicolas

Publikováno v: Signal Processing. 135:96-106

The method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smoothed 1/2 regularization term. As the mean of the noise in the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7701d359a9d600e5981db5cae2560ec8
https://doi.org/10.1016/j.sigpro.2016.12.022

Sparsity Optimization Method for Slow-Moving Landslides Detection in Satellite Image Time-Series

Autor: Marie-Pierre Doin, Pascal G. Lacroix, Mai Quyen Pham

Publikováno v: IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2018, pp.1-12. ⟨10.1109/TGRS.2018.2871550⟩
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (4), pp.2133-2144. ⟨10.1109/TGRS.2018.2871550⟩

International audience; This paper presents a new method based on recent optimization technique to detect slow-moving landslides (

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::efdfd47b3323b0b7f4633cd0ca550208
https://hal.archives-ouvertes.fr/hal-02062494

Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed ${\ell _1}/{\ell _2}$ Regularization

Autor: Jean-Christophe Pesquet, Laurent Duval, Emilie Chouzenoux, Audrey Repetti, Mai Quyen Pham

Publikováno v: IEEE Signal Processing Letters. 22:539-543

The l1/l2 ratio regularization function has shown good performance for retrieving sparse signals in a number of recent works, in the context of blind deconvolution. Indeed, it benefits from a scale invariance property much desirable in the blind cont

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::08e92b2a4707682dd0b4154dbc88ef82
https://doi.org/10.1109/lsp.2014.2362861

Sparse Deconvolution for Moving-Source Localization

Autor: Mai Quyen Pham, Jerome Mars, Barbara Nicolas, Benoit Oudompheng

Publikováno v: ICASSP 2016-Proceedings
ICASSP 2016-41st IEEE International Conference on Acoustics, Speech and Signal Processing
ICASSP 2016-41st IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2016, Shanghai, China. pp.355-359, ⟨10.1109/ICASSP.2016.7471696⟩
ICASSP

International audience; In this paper, we propose a method for moving-source localization based on beamforming output and on sparse representation of the source positions. The goal of this method is to achieve spatial deconvolution of the beamforming

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b126bec515a46d4c0ff21ecdbfa1b02
https://hal.archives-ouvertes.fr/hal-01343760/file/Pham_M_Icassp.pdf

A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal

Autor: Jean-Christophe Pesquet, Caroline Chaux, Laurent Duval, Mai Quyen Pham

Publikováno v: IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2014, 62 (16), pp.4256-4269. ⟨10.1109/TSP.2014.2331614⟩
IEEE Transactions on Signal Processing, 2014, 62 (16), pp.4256-4269. ⟨10.1109/TSP.2014.2331614⟩

International audience; Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62485ffb0d843a257229fd839e649154
https://hal.archives-ouvertes.fr/hal-00914628/document